Abstract
This script using qiime2R package to visualize Qiime2 artifacts (.qza files) and do post Qiime2 analysis, including ploting PCoA, drawing taxa heatmap and barplot, differential abundance analysis, ploting phylogenetic tree etc.
Load qiime2 artifacts and metadata
proj = "Jenny_16SrRNA_20220106"
# read feature table
ASVs <- read_qza(paste0("asv/dada2/", proj, "-asv-table.qza"))
cat("# Show 5 samples and first 5 taxa:\n")## # Show 5 samples and first 5 taxa:ASVs$data[1:5,6:10]## Noto.14.10G Noto.14.11F Noto.14.11G
## 3ccdc88a8737c3ac98bd0a40b1b93bf8 0 4900 256
## 35f3ce6a0ee2829e6ccb193be3c4338b 0 10059 245
## 1930d2ae4018583d606e705beea31bd1 19 1704 432
## 5292f29cab69c370997bcb039ef68d64 0 385 26
## df0e3d38eec730326754d8c17a8b8efe 1011 2904 2850
## Noto.14.12F Noto.14.12G
## 3ccdc88a8737c3ac98bd0a40b1b93bf8 1084 1550
## 35f3ce6a0ee2829e6ccb193be3c4338b 2865 556
## 1930d2ae4018583d606e705beea31bd1 529 2319
## 5292f29cab69c370997bcb039ef68d64 77 210
## df0e3d38eec730326754d8c17a8b8efe 623 3592# read metadata
metadata <- read_q2metadata(paste0("../", proj, "_metadata.tsv"))
# samples are removed due to low abundance
rmsamples <- c(
'Noto.16.18G',
'Noto.16.9G',
'Noto.16.14G',
'Noto.16.1G',
'Noto.14.26G',
'Noto.14.4G',
'Noto.14.2G',
'Noto.16.12G',
'Noto.16.13G',
'Noto.16.3G',
'DNAfreewater3.20211116',
'Extractemptywell3.20211116',
'Noto.14.22G',
'Noto.16.20G')
metadata <- metadata %>% filter(!SampleID %in% rmsamples)
cat("# Here is what metadata looks like:\n")## # Here is what metadata looks like:head(metadata)## SampleID Sample_type Mouse_background Infection
## 1 Noto.14.1G gastric tissue FVBN Uninfected
## 2 Noto.14.3G gastric tissue FVBN Uninfected
## 3 Noto.14.5G gastric tissue FVBN Uninfected
## 4 Noto.14.6G gastric tissue FVBN Uninfected
## 5 Noto.14.7G gastric tissue FVBN Uninfected
## 6 Noto.14.8G gastric tissue FVBN Uninfected
## Diet_Or_Water_treatment H. pylori_colonization
## 1 PicoLab Rodent Diet 5L0D* (standard) negative
## 2 PicoLab Rodent Diet 5L0D* (standard) negative
## 3 PicoLab Rodent Diet 5L0D* (standard) negative
## 4 PicoLab Rodent Diet 5L0D* (standard) negative
## 5 PicoLab Rodent Diet 5L0D* (standard) negative
## 6 PicoLab Rodent Diet 5L0D* (standard) negative
## Inflammation_Score(0-12)
## 1 0
## 2 0
## 3 0
## 4 0
## 5 0
## 6 0# read taxonomy
taxonomy <- read_qza(paste0("asv/taxonomy/", proj, "-taxonomy.qza"))
taxonomy <- parse_taxonomy(taxonomy$data)
cat("# Taxonomy assignment:\n")## # Taxonomy assignment:head(taxonomy)## Kingdom Phylum Class
## 3ccdc88a8737c3ac98bd0a40b1b93bf8 Bacteria Bacteroidetes Bacteroidia
## 35f3ce6a0ee2829e6ccb193be3c4338b Bacteria Tenericutes Mollicutes
## 1930d2ae4018583d606e705beea31bd1 Bacteria Bacteroidetes Bacteroidia
## 5292f29cab69c370997bcb039ef68d64 Bacteria Bacteroidetes Bacteroidia
## df0e3d38eec730326754d8c17a8b8efe Bacteria Firmicutes Bacilli
## 0df6c802966e8670279671824da4f10a Bacteria Firmicutes Bacilli
## Order Family
## 3ccdc88a8737c3ac98bd0a40b1b93bf8 Bacteroidales Rikenellaceae
## 35f3ce6a0ee2829e6ccb193be3c4338b Anaeroplasmatales Anaeroplasmataceae
## 1930d2ae4018583d606e705beea31bd1 Bacteroidales S24-7
## 5292f29cab69c370997bcb039ef68d64 Bacteroidales Bacteroidaceae
## df0e3d38eec730326754d8c17a8b8efe Turicibacterales Turicibacteraceae
## 0df6c802966e8670279671824da4f10a Lactobacillales Lactobacillaceae
## Genus Species
## 3ccdc88a8737c3ac98bd0a40b1b93bf8 <NA> <NA>
## 35f3ce6a0ee2829e6ccb193be3c4338b Anaeroplasma <NA>
## 1930d2ae4018583d606e705beea31bd1 <NA> <NA>
## 5292f29cab69c370997bcb039ef68d64 Bacteroides <NA>
## df0e3d38eec730326754d8c17a8b8efe Turicibacter <NA>
## 0df6c802966e8670279671824da4f10a Lactobacillus <NA># create Phyloseq object
# physeq<-qza_to_phyloseq(
# features=paste0("asv/dada2/", proj, "-asv-table.qza"),
# tree=paste0("asv/phylogeny/", proj, "-rooted_tree.qza"),
# taxonomy=paste0("asv/taxonomy/", proj, "-taxonomy.qza"),
# metadata = paste0("../", proj, "_metadata.tsv")
# )
# cat("# create a Phyloseq object, which includes the following stuffs: \n
# (OTU table is actually ASV table when you do ASV) \n")
# physeqPost-Qiime2 analysis - Visualizations
- Alpha diversity between subgroups
shannon <- read_qza(paste0("asv/diversity/core-metrics-results/", proj, "-shannon-vector.qza"))
shannon <- shannon$data %>% rownames_to_column("SampleID")
metadata <- metadata %>% left_join(shannon)
head(metadata)## SampleID Sample_type Mouse_background Infection
## 1 Noto.14.1G gastric tissue FVBN Uninfected
## 2 Noto.14.3G gastric tissue FVBN Uninfected
## 3 Noto.14.5G gastric tissue FVBN Uninfected
## 4 Noto.14.6G gastric tissue FVBN Uninfected
## 5 Noto.14.7G gastric tissue FVBN Uninfected
## 6 Noto.14.8G gastric tissue FVBN Uninfected
## Diet_Or_Water_treatment H. pylori_colonization
## 1 PicoLab Rodent Diet 5L0D* (standard) negative
## 2 PicoLab Rodent Diet 5L0D* (standard) negative
## 3 PicoLab Rodent Diet 5L0D* (standard) negative
## 4 PicoLab Rodent Diet 5L0D* (standard) negative
## 5 PicoLab Rodent Diet 5L0D* (standard) negative
## 6 PicoLab Rodent Diet 5L0D* (standard) negative
## Inflammation_Score(0-12) shannon
## 1 0 2.198340
## 2 0 2.283360
## 3 0 1.829425
## 4 0 2.490648
## 5 0 2.207579
## 6 0 2.039649- Boxplots for each comparison in Fecal samples:
- #1 b.compare 14.1 F-14.9 F (uninfected WT FVBN mice) vs. 14.10 F-14.14 F (uninfected INS-GAS mice with gastrin transgene)
- #2 b.compare 14.15 F-14.19 F (uninfected INS-GAS mice on iron-replete diet) vs. 14.20 F-14.26 F (uninfected INS-GAS mice on iron-depleted diet).
- #3 b.compare 16.1 F – 16.5 F (uninfected INS-GAS mice given water alone) vs. 16.6 F – 16.10 F (uninfected INS-GAS mice given water supplemented with 100 uM DCA).
- #4 c.compare 16.1 F – 16.5 F (uninfected INS-GAS mice given water alone) vs. 16.11 F – 16.15 F (H. pylori infected INS-GAS mice given water alone)
- #4 d.compare 16.6 F- 16.10 F (uninfected INS-GAS mice give water supplemented with 100 uM DCA) vs. 16.16 F – 16.20 F (H. pylori infected INS-GAS mice give water supplemented with 100 uM DCA).
- #1 b.compare 14.1 F-14.9 F (uninfected WT FVBN mice) vs. 14.10 F-14.14 F (uninfected INS-GAS mice with gastrin transgene)
# plot each comparison and do t-test
Fcomp1 <- metadata %>% filter(Sample_type=='fecal pellet' & Diet_Or_Water_treatment=='PicoLab Rodent Diet 5L0D* (standard)')
t_pvalue1 <- paste0("#1b. t-test pvalue: ", t.test(shannon ~ Mouse_background, data=Fcomp1)$p.value, "\n")
Fcomp1## SampleID Sample_type Mouse_background Infection
## 1 Noto.14.1F fecal pellet FVBN Uninfected
## 2 Noto.14.2F fecal pellet FVBN Uninfected
## 3 Noto.14.3F fecal pellet FVBN Uninfected
## 4 Noto.14.4F fecal pellet FVBN Uninfected
## 5 Noto.14.5F fecal pellet FVBN Uninfected
## 6 Noto.14.6F fecal pellet FVBN Uninfected
## 7 Noto.14.7F fecal pellet FVBN Uninfected
## 8 Noto.14.8F fecal pellet FVBN Uninfected
## 9 Noto.14.9F fecal pellet FVBN Uninfected
## 10 Noto.14.10F fecal pellet INS-GAS Uninfected
## 11 Noto.14.11F fecal pellet INS-GAS Uninfected
## 12 Noto.14.12F fecal pellet INS-GAS Uninfected
## 13 Noto.14.13F fecal pellet INS-GAS Uninfected
## 14 Noto.14.14F fecal pellet INS-GAS Uninfected
## Diet_Or_Water_treatment H. pylori_colonization
## 1 PicoLab Rodent Diet 5L0D* (standard) <NA>
## 2 PicoLab Rodent Diet 5L0D* (standard) <NA>
## 3 PicoLab Rodent Diet 5L0D* (standard) <NA>
## 4 PicoLab Rodent Diet 5L0D* (standard) <NA>
## 5 PicoLab Rodent Diet 5L0D* (standard) <NA>
## 6 PicoLab Rodent Diet 5L0D* (standard) <NA>
## 7 PicoLab Rodent Diet 5L0D* (standard) <NA>
## 8 PicoLab Rodent Diet 5L0D* (standard) <NA>
## 9 PicoLab Rodent Diet 5L0D* (standard) <NA>
## 10 PicoLab Rodent Diet 5L0D* (standard) <NA>
## 11 PicoLab Rodent Diet 5L0D* (standard) <NA>
## 12 PicoLab Rodent Diet 5L0D* (standard) <NA>
## 13 PicoLab Rodent Diet 5L0D* (standard) <NA>
## 14 PicoLab Rodent Diet 5L0D* (standard) <NA>
## Inflammation_Score(0-12) shannon
## 1 <NA> 5.744005
## 2 <NA> 5.687151
## 3 <NA> 5.033283
## 4 <NA> 5.510055
## 5 <NA> 5.800220
## 6 <NA> 5.893487
## 7 <NA> 6.067828
## 8 <NA> 6.054099
## 9 <NA> 6.400792
## 10 <NA> 6.196199
## 11 <NA> 5.899325
## 12 <NA> 5.435134
## 13 <NA> 6.385310
## 14 <NA> 6.186501# pdf("Shannon_diversity_comparisons_Fecal.pdf", 2.5, 5)
Fcomp1 %>% ggplot(aes(x=Mouse_background, y=shannon, fill=Mouse_background)) +
stat_summary(geom="bar", fun.data=mean_se, color="black") +
geom_jitter(shape=21, width=0.2, height=0, size = 3, color="red")+
theme_classic() +
theme(legend.position = "none", text = element_text(family = "Helvetica")) +
scale_fill_manual(values = c("black", "darkgrey")) +
ylab("Shannon diversity") +
xlab("Mouse background")
Fcomp2 <- metadata %>% filter(Sample_type=='fecal pellet' & grepl('TestDiet', Diet_Or_Water_treatment))
t_pvalue2 <- paste0("#2b. t-test pvalue: ", t.test(shannon ~ Diet_Or_Water_treatment, data=Fcomp2)$p.value, "\n")
Fcomp2 <- Fcomp2 %>% dplyr::mutate(Diet_Or_Water_treatment = str_split(Diet_Or_Water_treatment, " ", simplify=TRUE)[ ,3])
Fcomp2$Diet_Or_Water_treatment <- factor(Fcomp2$Diet_Or_Water_treatment, levels=c("Iron-replete", "Iron-depleted"))
Fcomp2## SampleID Sample_type Mouse_background Infection Diet_Or_Water_treatment
## 1 Noto.14.15F fecal pellet INS-GAS Uninfected Iron-replete
## 2 Noto.14.16F fecal pellet INS-GAS Uninfected Iron-replete
## 3 Noto.14.17F fecal pellet INS-GAS Uninfected Iron-replete
## 4 Noto.14.18F fecal pellet INS-GAS Uninfected Iron-replete
## 5 Noto.14.19F fecal pellet INS-GAS Uninfected Iron-replete
## 6 Noto.14.20F fecal pellet INS-GAS Uninfected Iron-depleted
## 7 Noto.14.21F fecal pellet INS-GAS Uninfected Iron-depleted
## 8 Noto.14.22F fecal pellet INS-GAS Uninfected Iron-depleted
## 9 Noto.14.23F fecal pellet INS-GAS Uninfected Iron-depleted
## 10 Noto.14.24F fecal pellet INS-GAS Uninfected Iron-depleted
## 11 Noto.14.25F fecal pellet INS-GAS Uninfected Iron-depleted
## 12 Noto.14.26F fecal pellet INS-GAS Uninfected Iron-depleted
## H. pylori_colonization Inflammation_Score(0-12) shannon
## 1 <NA> <NA> 3.912590
## 2 <NA> <NA> 4.267224
## 3 <NA> <NA> 4.312662
## 4 <NA> <NA> 4.455727
## 5 <NA> <NA> 4.703006
## 6 <NA> <NA> 4.436776
## 7 <NA> <NA> 4.804841
## 8 <NA> <NA> 4.990394
## 9 <NA> <NA> 4.652954
## 10 <NA> <NA> 4.316602
## 11 <NA> <NA> 4.246618
## 12 <NA> <NA> 4.288628Fcomp2 %>% ggplot(aes(x=Diet_Or_Water_treatment, y=shannon, fill=Diet_Or_Water_treatment)) +
stat_summary(geom="bar", fun.data=mean_se, color="black") +
geom_jitter(shape=21, width=0.2, height=0, size = 3, color="red")+
theme_classic() +
theme(legend.position = "none", text = element_text(family = "Helvetica")) +
scale_fill_manual(values = c("black", "darkgrey")) +
ylab("Shannon diversity") +
xlab("Iron supply")
Fcomp3 <- metadata %>% filter(Sample_type=='fecal pellet' & grepl('H20', Diet_Or_Water_treatment) & Infection=="Uninfected")
t_pvalue3 <- paste0("#3b. t-test pvalue: ", t.test(shannon ~ Diet_Or_Water_treatment, data=Fcomp3)$p.value, "\n")
Fcomp3## SampleID Sample_type Mouse_background Infection Diet_Or_Water_treatment
## 1 Noto.16.1F fecal pellet INS-GAS Uninfected H20
## 2 Noto.16.2F fecal pellet INS-GAS Uninfected H20
## 3 Noto.16.3F fecal pellet INS-GAS Uninfected H20
## 4 Noto.16.4F fecal pellet INS-GAS Uninfected H20
## 5 Noto.16.5F fecal pellet INS-GAS Uninfected H20
## 6 Noto.16.6F fecal pellet INS-GAS Uninfected 100 mM DCA in H20
## 7 Noto.16.7F fecal pellet INS-GAS Uninfected 100 mM DCA in H20
## 8 Noto.16.8F fecal pellet INS-GAS Uninfected 100 mM DCA in H20
## 9 Noto.16.9F fecal pellet INS-GAS Uninfected 100 mM DCA in H20
## 10 Noto.16.10F fecal pellet INS-GAS Uninfected 100 mM DCA in H20
## H. pylori_colonization Inflammation_Score(0-12) shannon
## 1 <NA> <NA> 5.510795
## 2 <NA> <NA> 5.611329
## 3 <NA> <NA> 5.646711
## 4 <NA> <NA> 5.922997
## 5 <NA> <NA> 5.835692
## 6 <NA> <NA> 5.752106
## 7 <NA> <NA> 5.189827
## 8 <NA> <NA> 5.233659
## 9 <NA> <NA> 5.630338
## 10 <NA> <NA> 5.208054Fcomp3 %>% ggplot(aes(x=Diet_Or_Water_treatment, y=shannon, fill=Diet_Or_Water_treatment)) +
stat_summary(geom="bar", fun.data=mean_se, color="black") +
geom_jitter(shape=21, width=0.2, height=0, size = 3, color="red")+
theme_classic() +
theme(legend.position = "none", text = element_text(family = "Helvetica")) +
scale_fill_manual(values = c("black", "darkgrey")) +
ylab("Shannon diversity") +
xlab("Water supply")
Fcomp4 <- metadata %>% filter(Sample_type=='fecal pellet' & grepl('^H20', Diet_Or_Water_treatment))
t_pvalue4 <- paste0("#4c. t-test pvalue: ", t.test(shannon ~ Infection, data=Fcomp4)$p.value, "\n")
Fcomp4## SampleID Sample_type Mouse_background Infection
## 1 Noto.16.1F fecal pellet INS-GAS Uninfected
## 2 Noto.16.2F fecal pellet INS-GAS Uninfected
## 3 Noto.16.3F fecal pellet INS-GAS Uninfected
## 4 Noto.16.4F fecal pellet INS-GAS Uninfected
## 5 Noto.16.5F fecal pellet INS-GAS Uninfected
## 6 Noto.16.11F fecal pellet INS-GAS H. pylori PMSS1
## 7 Noto.16.12F fecal pellet INS-GAS H. pylori PMSS1
## 8 Noto.16.13F fecal pellet INS-GAS H. pylori PMSS1
## 9 Noto.16.14F fecal pellet INS-GAS H. pylori PMSS1
## 10 Noto.16.15F fecal pellet INS-GAS H. pylori PMSS1
## Diet_Or_Water_treatment H. pylori_colonization Inflammation_Score(0-12)
## 1 H20 <NA> <NA>
## 2 H20 <NA> <NA>
## 3 H20 <NA> <NA>
## 4 H20 <NA> <NA>
## 5 H20 <NA> <NA>
## 6 H20 <NA> <NA>
## 7 H20 <NA> <NA>
## 8 H20 <NA> <NA>
## 9 H20 <NA> <NA>
## 10 H20 <NA> <NA>
## shannon
## 1 5.510795
## 2 5.611329
## 3 5.646711
## 4 5.922997
## 5 5.835692
## 6 5.740818
## 7 5.572455
## 8 5.568770
## 9 5.502304
## 10 5.182336Fcomp4 %>% ggplot(aes(x=Infection, y=shannon, fill=Infection)) +
stat_summary(geom="bar", fun.data=mean_se, color="black") +
geom_jitter(shape=21, width=0.2, height=0, size = 3, color="red")+
theme_classic() +
theme(legend.position = "none", text = element_text(family = "Helvetica")) +
scale_fill_manual(values = c("black", "darkgrey")) +
ylab("Shannon diversity") +
xlab("Infection")
Fcomp5 <- metadata %>% filter(Sample_type=='fecal pellet' & grepl('DCA', Diet_Or_Water_treatment))
t_pvalue5 <- paste0("#4d. t-test pvalue: ", t.test(shannon ~ Infection, data=Fcomp5)$p.value, "\n")
Fcomp5## SampleID Sample_type Mouse_background Infection
## 1 Noto.16.6F fecal pellet INS-GAS Uninfected
## 2 Noto.16.7F fecal pellet INS-GAS Uninfected
## 3 Noto.16.8F fecal pellet INS-GAS Uninfected
## 4 Noto.16.9F fecal pellet INS-GAS Uninfected
## 5 Noto.16.10F fecal pellet INS-GAS Uninfected
## 6 Noto.16.16F fecal pellet INS-GAS H. pylori PMSS1
## 7 Noto.16.17F fecal pellet INS-GAS H. pylori PMSS1
## 8 Noto.16.18F fecal pellet INS-GAS H. pylori PMSS1
## 9 Noto.16.19F fecal pellet INS-GAS H. pylori PMSS1
## 10 Noto.16.20F fecal pellet INS-GAS H. pylori PMSS1
## Diet_Or_Water_treatment H. pylori_colonization Inflammation_Score(0-12)
## 1 100 mM DCA in H20 <NA> <NA>
## 2 100 mM DCA in H20 <NA> <NA>
## 3 100 mM DCA in H20 <NA> <NA>
## 4 100 mM DCA in H20 <NA> <NA>
## 5 100 mM DCA in H20 <NA> <NA>
## 6 100 mM DCA in H20 <NA> <NA>
## 7 100 mM DCA in H20 <NA> <NA>
## 8 100 mM DCA in H20 <NA> <NA>
## 9 100 mM DCA in H20 <NA> <NA>
## 10 100 mM DCA in H20 <NA> <NA>
## shannon
## 1 5.752106
## 2 5.189827
## 3 5.233659
## 4 5.630338
## 5 5.208054
## 6 6.025388
## 7 5.513765
## 8 5.862775
## 9 5.714661
## 10 5.233691Fcomp5 %>% ggplot(aes(x=Infection, y=shannon, fill=Infection)) +
stat_summary(geom="bar", fun.data=mean_se, color="black") +
geom_jitter(shape=21, width=0.2, height=0, size = 3, color="red")+
theme_classic() +
theme(legend.position = "none", text = element_text(family = "Helvetica")) +
scale_fill_manual(values = c("black", "darkgrey")) +
ylab("Shannon diversity") +
xlab("Infection")
# dev.off()
cat(t_pvalue1, t_pvalue2, t_pvalue3, t_pvalue4, t_pvalue5)## #1b. t-test pvalue: 0.319522057532681
## #2b. t-test pvalue: 0.259171096086067
## #3b. t-test pvalue: 0.0710030837086846
## #4c. t-test pvalue: 0.145904134898372
## #4d. t-test pvalue: 0.181916682303912- Boxplots for each comparison in Gatric samples:
- #1 a.compare 14.1 G-14.9 G (uninfected WT FVBN mice) vs. 14.10 G-14.14 G (uninfected INS-GAS mice with gastrin transgene).
- #2 a.compare 14.15 G-14.19 G (uninfected INS-GAS mice on iron-replete diet) vs. 14.20 G-14.26 G (uninfected INS-GAS mice on iron-depleted diet).
- #3 a.compare 16.1 G – 16.5 G (uninfected INS-GAS mice given water alone) vs. 16.6 G – 16.10 G (uninfected INS-GAS mice give water supplemented with 100 uM DCA).
- #4 a.compare 16.1 G – 16.5 G (uninfected INS-GAS mice given water alone) vs. 16.11 G – 16.15 G (H. pylori infected INS-GAS mice given water alone)
- #4 b.compare 16.6 G- 16.10 G (uninfected INS-GAS mice give water supplemented with 100 uM DCA) vs. 16.16 G – 16.20 G (H. pylori infected INS-GAS mice give water supplemented with 100 uM DCA).
# plot each comparison and do t-test
Gcomp1 <- metadata %>% filter(Sample_type=='gastric tissue' & Diet_Or_Water_treatment=='PicoLab Rodent Diet 5L0D* (standard)')
t_pvalue1 <- paste0("#1a. t-test pvalue: ", t.test(shannon ~ Mouse_background, data=Gcomp1)$p.value, "\n")
# pdf("Shannon_diversity_comparisons_Gastric.pdf", 2.5, 5)
Gcomp1## SampleID Sample_type Mouse_background Infection
## 1 Noto.14.1G gastric tissue FVBN Uninfected
## 2 Noto.14.3G gastric tissue FVBN Uninfected
## 3 Noto.14.5G gastric tissue FVBN Uninfected
## 4 Noto.14.6G gastric tissue FVBN Uninfected
## 5 Noto.14.7G gastric tissue FVBN Uninfected
## 6 Noto.14.8G gastric tissue FVBN Uninfected
## 7 Noto.14.9G gastric tissue FVBN Uninfected
## 8 Noto.14.10G gastric tissue INS-GAS Uninfected
## 9 Noto.14.11G gastric tissue INS-GAS Uninfected
## 10 Noto.14.12G gastric tissue INS-GAS Uninfected
## 11 Noto.14.13G gastric tissue INS-GAS Uninfected
## 12 Noto.14.14G gastric tissue INS-GAS Uninfected
## Diet_Or_Water_treatment H. pylori_colonization
## 1 PicoLab Rodent Diet 5L0D* (standard) negative
## 2 PicoLab Rodent Diet 5L0D* (standard) negative
## 3 PicoLab Rodent Diet 5L0D* (standard) negative
## 4 PicoLab Rodent Diet 5L0D* (standard) negative
## 5 PicoLab Rodent Diet 5L0D* (standard) negative
## 6 PicoLab Rodent Diet 5L0D* (standard) negative
## 7 PicoLab Rodent Diet 5L0D* (standard) negative
## 8 PicoLab Rodent Diet 5L0D* (standard) negative
## 9 PicoLab Rodent Diet 5L0D* (standard) negative
## 10 PicoLab Rodent Diet 5L0D* (standard) negative
## 11 PicoLab Rodent Diet 5L0D* (standard) negative
## 12 PicoLab Rodent Diet 5L0D* (standard) negative
## Inflammation_Score(0-12) shannon
## 1 0 2.198340
## 2 0 2.283360
## 3 0 1.829425
## 4 0 2.490648
## 5 0 2.207579
## 6 0 2.039649
## 7 0.5 2.208223
## 8 0 2.175170
## 9 0 5.412382
## 10 0 5.456346
## 11 0 4.203536
## 12 0 1.898425Gcomp1 %>% ggplot(aes(x=Mouse_background, y=shannon, fill=Mouse_background)) +
stat_summary(geom="bar", fun.data=mean_se, color="black") +
geom_jitter(shape=21, width=0.2, height=0, size = 3, color="red")+
theme_classic() +
theme(legend.position = "none", text = element_text(family = "Helvetica")) +
scale_fill_manual(values = c("black", "darkgrey")) +
ylab("Shannon diversity") +
xlab("Mouse background")
Gcomp2 <- metadata %>% filter(Sample_type=='gastric tissue' & grepl('TestDiet', Diet_Or_Water_treatment))
t_pvalue2 <- paste0("#2a. t-test pvalue: ", t.test(shannon ~ Diet_Or_Water_treatment, data=Gcomp2)$p.value, "\n")
Gcomp2 <- Gcomp2 %>% dplyr::mutate(Diet_Or_Water_treatment = str_split(Diet_Or_Water_treatment, " ", simplify=TRUE)[ ,3])
Gcomp2$Diet_Or_Water_treatment <- factor(Gcomp2$Diet_Or_Water_treatment, levels=c("Iron-replete", "Iron-depleted"))
Gcomp2## SampleID Sample_type Mouse_background Infection
## 1 Noto.14.15G gastric tissue INS-GAS Uninfected
## 2 Noto.14.16G gastric tissue INS-GAS Uninfected
## 3 Noto.14.17G gastric tissue INS-GAS Uninfected
## 4 Noto.14.18G gastric tissue INS-GAS Uninfected
## 5 Noto.14.19G gastric tissue INS-GAS Uninfected
## 6 Noto.14.20G gastric tissue INS-GAS Uninfected
## 7 Noto.14.21G gastric tissue INS-GAS Uninfected
## 8 Noto.14.23G gastric tissue INS-GAS Uninfected
## 9 Noto.14.24G gastric tissue INS-GAS Uninfected
## 10 Noto.14.25G gastric tissue INS-GAS Uninfected
## Diet_Or_Water_treatment H. pylori_colonization Inflammation_Score(0-12)
## 1 Iron-replete negative 0
## 2 Iron-replete negative 0
## 3 Iron-replete negative 0
## 4 Iron-replete negative 0
## 5 Iron-replete negative 0
## 6 Iron-depleted negative 0
## 7 Iron-depleted negative 0
## 8 Iron-depleted negative 0
## 9 Iron-depleted negative 0.5
## 10 Iron-depleted negative 0
## shannon
## 1 2.805109
## 2 2.890866
## 3 2.927809
## 4 1.914683
## 5 2.000384
## 6 2.018251
## 7 1.562809
## 8 4.143961
## 9 2.352939
## 10 4.711281Gcomp2 %>% ggplot(aes(x=Diet_Or_Water_treatment, y=shannon, fill=Diet_Or_Water_treatment)) +
stat_summary(geom="bar", fun.data=mean_se, color="black") +
geom_jitter(shape=21, width=0.2, height=0, size = 3, color="red")+
theme_classic() +
theme(legend.position = "none", text = element_text(family = "Helvetica")) +
scale_fill_manual(values = c("black", "darkgrey")) +
ylab("Shannon diversity") +
xlab("Iron supply")
Gcomp3 <- metadata %>% filter(Sample_type=='gastric tissue' & grepl('H20', Diet_Or_Water_treatment) & Infection=="Uninfected")
t_pvalue3 <- paste0("#3a. t-test pvalue: ", t.test(shannon ~ Diet_Or_Water_treatment, data=Gcomp3)$p.value, "\n")
Gcomp3## SampleID Sample_type Mouse_background Infection
## 1 Noto.16.2G gastric tissue INS-GAS Uninfected
## 2 Noto.16.4G gastric tissue INS-GAS Uninfected
## 3 Noto.16.5G gastric tissue INS-GAS Uninfected
## 4 Noto.16.6G gastric tissue INS-GAS Uninfected
## 5 Noto.16.7G gastric tissue INS-GAS Uninfected
## 6 Noto.16.8G gastric tissue INS-GAS Uninfected
## 7 Noto.16.10G gastric tissue INS-GAS Uninfected
## Diet_Or_Water_treatment H. pylori_colonization Inflammation_Score(0-12)
## 1 H20 negative pending
## 2 H20 negative pending
## 3 H20 negative pending
## 4 100 mM DCA in H20 negative pending
## 5 100 mM DCA in H20 negative pending
## 6 100 mM DCA in H20 negative pending
## 7 100 mM DCA in H20 negative pending
## shannon
## 1 3.905627
## 2 4.696699
## 3 1.428787
## 4 1.420909
## 5 1.554961
## 6 1.529912
## 7 1.327314Gcomp3 %>% ggplot(aes(x=Diet_Or_Water_treatment, y=shannon, fill=Diet_Or_Water_treatment)) +
stat_summary(geom="bar", fun.data=mean_se, color="black") +
geom_jitter(shape=21, width=0.2, height=0, size = 3, color="red")+
theme_classic() +
theme(legend.position = "none", text = element_text(family = "Helvetica")) +
scale_fill_manual(values = c("black", "darkgrey")) +
ylab("Shannon diversity") +
xlab("Water supply")
Gcomp4 <- metadata %>% filter(Sample_type=='gastric tissue' & grepl('^H20', Diet_Or_Water_treatment))
t_pvalue4 <- paste0("#4a. t-test pvalue: ", t.test(shannon ~ Infection, data=Gcomp4)$p.value, "\n")
Gcomp4## SampleID Sample_type Mouse_background Infection
## 1 Noto.16.2G gastric tissue INS-GAS Uninfected
## 2 Noto.16.4G gastric tissue INS-GAS Uninfected
## 3 Noto.16.5G gastric tissue INS-GAS Uninfected
## 4 Noto.16.11G gastric tissue INS-GAS H. pylori PMSS1
## 5 Noto.16.15G gastric tissue INS-GAS H. pylori PMSS1
## Diet_Or_Water_treatment H. pylori_colonization Inflammation_Score(0-12)
## 1 H20 negative pending
## 2 H20 negative pending
## 3 H20 negative pending
## 4 H20 below the limit of detection pending
## 5 H20 84000 CFU/g pending
## shannon
## 1 3.905627
## 2 4.696699
## 3 1.428787
## 4 1.211462
## 5 1.427284Gcomp4 %>% ggplot(aes(x=Infection, y=shannon, fill=Infection)) +
stat_summary(geom="bar", fun.data=mean_se, color="black") +
geom_jitter(shape=21, width=0.2, height=0, size = 3, color="red")+
theme_classic() +
theme(legend.position = "none", text = element_text(family = "Helvetica")) +
scale_fill_manual(values = c("black", "darkgrey")) +
ylab("Shannon diversity") +
xlab("Infection")
Gcomp5 <- metadata %>% filter(Sample_type=='gastric tissue' & grepl('DCA', Diet_Or_Water_treatment))
t_pvalue5 <- paste0("#4b. t-test pvalue: ", t.test(shannon ~ Infection, data=Gcomp5)$p.value, "\n")
Gcomp5## SampleID Sample_type Mouse_background Infection
## 1 Noto.16.6G gastric tissue INS-GAS Uninfected
## 2 Noto.16.7G gastric tissue INS-GAS Uninfected
## 3 Noto.16.8G gastric tissue INS-GAS Uninfected
## 4 Noto.16.10G gastric tissue INS-GAS Uninfected
## 5 Noto.16.16G gastric tissue INS-GAS H. pylori PMSS1
## 6 Noto.16.17G gastric tissue INS-GAS H. pylori PMSS1
## 7 Noto.16.19G gastric tissue INS-GAS H. pylori PMSS1
## Diet_Or_Water_treatment H. pylori_colonization Inflammation_Score(0-12)
## 1 100 mM DCA in H20 negative pending
## 2 100 mM DCA in H20 negative pending
## 3 100 mM DCA in H20 negative pending
## 4 100 mM DCA in H20 negative pending
## 5 100 mM DCA in H20 below the limit of detection pending
## 6 100 mM DCA in H20 below the limit of detection pending
## 7 100 mM DCA in H20 7143 CFU/g pending
## shannon
## 1 1.420909
## 2 1.554961
## 3 1.529912
## 4 1.327314
## 5 3.798749
## 6 5.713245
## 7 4.289579Gcomp5 %>% ggplot(aes(x=Infection, y=shannon, fill=Infection)) +
stat_summary(geom="bar", fun.data=mean_se, color="black") +
geom_jitter(shape=21, width=0.2, height=0, size = 3, color="red")+
theme_classic() +
theme(legend.position = "none", text = element_text(family = "Helvetica")) +
scale_fill_manual(values = c("black", "darkgrey")) +
ylab("Shannon diversity") +
xlab("Infection")
# dev.off()
cat(t_pvalue1, t_pvalue2, t_pvalue3, t_pvalue4, t_pvalue5)## #1a. t-test pvalue: 0.0976543942567185
## #2a. t-test pvalue: 0.52498013961145
## #3a. t-test pvalue: 0.195192597999423
## #4a. t-test pvalue: 0.174618728891541
## #4b. t-test pvalue: 0.0309048607751814- Perform Permutational Multivariate Analysis of Variance (PERMANOVA) test for beta diversity difference among communities
- for fecal pellet:
wunifrac_dist <- read_qza(paste0("asv/dada2/filter/", proj, "-weighted_unifrac_distance_matrix_transgene_fecal_PicoLab_Rodent_Diet.qza"))
wu <- wunifrac_dist$data
## metadata has the SampleID and the groups to compare in one column, make sure the order aligns between the metadata and the distance matrix.
dist_toTest <- usedist::dist_subset(wu, Fcomp1$SampleID)
res_permanova <- vegan::adonis(formula = dist_toTest ~ Mouse_background, data = Fcomp1)
Fcomp1_permanova <- paste0("#1b. PERMANOVA pvalue: ", res_permanova$aov.tab$`Pr(>F)`[1], "\n")
wunifrac_dist <- read_qza(paste0("asv/dada2/filter/", proj, "-weighted_unifrac_distance_matrix_iron_deficiency_fecal.qza"))
wu <- wunifrac_dist$data
## metadata has the SampleID and the groups to compare in one column, make sure the order aligns between the metadata and the distance matrix.
dist_toTest <- usedist::dist_subset(wu, Fcomp2$SampleID)
res_permanova <- vegan::adonis(formula = dist_toTest ~ Diet_Or_Water_treatment, data = Fcomp2)
Fcomp2_permanova <- paste0("#2b. PERMANOVA pvalue: ", res_permanova$aov.tab$`Pr(>F)`[1], "\n")
wunifrac_dist <- read_qza(paste0("asv/dada2/filter/", proj,
"-weighted_unifrac_distance_matrix_DCA_fecal_uninfected.qza"))
wu <- wunifrac_dist$data
## metadata has the SampleID and the groups to compare in one column, make sure the order aligns between the metadata and the distance matrix.
dist_toTest <- usedist::dist_subset(wu, Fcomp3$SampleID)
res_permanova <- vegan::adonis(formula = dist_toTest ~ Diet_Or_Water_treatment, data = Fcomp3)
Fcomp3_permanova <- paste0("#3b. PERMANOVA pvalue: ", res_permanova$aov.tab$`Pr(>F)`[1], "\n")
wunifrac_dist <- read_qza(paste0("asv/dada2/filter/", proj,
"-weighted_unifrac_distance_matrix_infection_fecal_H20.qza"))
wu <- wunifrac_dist$data
## metadata has the SampleID and the groups to compare in one column, make sure the order aligns between the metadata and the distance matrix.
dist_toTest <- usedist::dist_subset(wu, Fcomp4$SampleID)
res_permanova <- vegan::adonis(formula = dist_toTest ~ Infection, data = Fcomp4)
Fcomp4_permanova <- paste0("#4c. PERMANOVA pvalue: ", res_permanova$aov.tab$`Pr(>F)`[1], "\n")
wunifrac_dist <- read_qza(paste0("asv/dada2/filter/", proj,
"-weighted_unifrac_distance_matrix_infection_fecal_DCA.qza"))
wu <- wunifrac_dist$data
## metadata has the SampleID and the groups to compare in one column, make sure the order aligns between the metadata and the distance matrix.
dist_toTest <- usedist::dist_subset(wu, Fcomp5$SampleID)
res_permanova <- vegan::adonis(formula = dist_toTest ~ Infection, data = Fcomp5)
Fcomp5_permanova <- paste0("#4d. PERMANOVA pvalue: ", res_permanova$aov.tab$`Pr(>F)`[1], "\n")
cat(Fcomp1_permanova, Fcomp2_permanova, Fcomp3_permanova, Fcomp4_permanova, Fcomp5_permanova)## #1b. PERMANOVA pvalue: 0.003
## #2b. PERMANOVA pvalue: 0.003
## #3b. PERMANOVA pvalue: 0.149
## #4c. PERMANOVA pvalue: 0.336
## #4d. PERMANOVA pvalue: 0.05- for gastric tissue:
wunifrac_dist <- read_qza(paste0("asv/dada2/filter/", proj, "-weighted_unifrac_distance_matrix_transgene_gastric_PicoLab_Rodent_Diet.qza"))
wu <- wunifrac_dist$data
## metadata has the SampleID and the groups to compare in one column, make sure the order aligns between the metadata and the distance matrix.
dist_toTest <- usedist::dist_subset(wu, Gcomp1$SampleID)
res_permanova <- vegan::adonis(formula = dist_toTest ~ Mouse_background, data = Gcomp1)
Gcomp1_permanova <- paste0("#1a. PERMANOVA pvalue: ", res_permanova$aov.tab$`Pr(>F)`[1], "\n")
wunifrac_dist <- read_qza(paste0("asv/dada2/filter/", proj, "-weighted_unifrac_distance_matrix_iron_deficiency_gastric.qza"))
wu <- wunifrac_dist$data
## metadata has the SampleID and the groups to compare in one column, make sure the order aligns between the metadata and the distance matrix.
dist_toTest <- usedist::dist_subset(wu, Gcomp2$SampleID)
res_permanova <- vegan::adonis(formula = dist_toTest ~ Diet_Or_Water_treatment, data = Gcomp2)
Gcomp2_permanova <- paste0("#2a. PERMANOVA pvalue: ", res_permanova$aov.tab$`Pr(>F)`[1], "\n")
wunifrac_dist <- read_qza(paste0("asv/dada2/filter/", proj,
"-weighted_unifrac_distance_matrix_DCA_gastric_uninfected.qza"))
wu <- wunifrac_dist$data
## metadata has the SampleID and the groups to compare in one column, make sure the order aligns between the metadata and the distance matrix.
dist_toTest <- usedist::dist_subset(wu, Gcomp3$SampleID)
res_permanova <- vegan::adonis(formula = dist_toTest ~ Diet_Or_Water_treatment, data = Gcomp3)
Gcomp3_permanova <- paste0("#3a. PERMANOVA pvalue: ", res_permanova$aov.tab$`Pr(>F)`[1], "\n")
wunifrac_dist <- read_qza(paste0("asv/dada2/filter/", proj,
"-weighted_unifrac_distance_matrix_infection_gastric_H20.qza"))
wu <- wunifrac_dist$data
## metadata has the SampleID and the groups to compare in one column, make sure the order aligns between the metadata and the distance matrix.
dist_toTest <- usedist::dist_subset(wu, Gcomp4$SampleID)
res_permanova <- vegan::adonis(formula = dist_toTest ~ Infection, data = Gcomp4)
Gcomp4_permanova <- paste0("#4a. PERMANOVA pvalue: ", res_permanova$aov.tab$`Pr(>F)`[1], "\n")
wunifrac_dist <- read_qza(paste0("asv/dada2/filter/", proj,
"-weighted_unifrac_distance_matrix_infection_gastric_DCA.qza"))
wu <- wunifrac_dist$data
## metadata has the SampleID and the groups to compare in one column, make sure the order aligns between the metadata and the distance matrix.
dist_toTest <- usedist::dist_subset(wu, Gcomp5$SampleID)
res_permanova <- vegan::adonis(formula = dist_toTest ~ Infection, data = Gcomp5)
Gcomp5_permanova <- paste0("#4b. PERMANOVA pvalue: ", res_permanova$aov.tab$`Pr(>F)`[1], "\n")
cat(Gcomp1_permanova, Gcomp2_permanova, Gcomp3_permanova, Gcomp4_permanova, Gcomp5_permanova)## #1a. PERMANOVA pvalue: 0.022
## #2a. PERMANOVA pvalue: 0.114
## #3a. PERMANOVA pvalue: 0.029
## #4a. PERMANOVA pvalue: 0.2
## #4b. PERMANOVA pvalue: 0.027- Plotting PCoA based on beta diversity matrices
- PCA for fecal pellet:
wunifrac_pcoa <- read_qza(paste0("asv/diversity/core-metrics-results/", proj, "-weighted-unifrac-pcoa-results.qza"))
PCs <- wunifrac_pcoa$data$Vectors %>% dplyr::select(SampleID, PC1, PC2)
# pdf("b_diversity_wunifrac_PCoA_Fecal.pdf", 5, 4)
PCs %>% right_join(Fcomp1) %>%
ggplot(aes(x=PC1, y=PC2, color=Mouse_background, size=shannon)) +
geom_point(alpha=0.8) + #alpha controls transparency and helps when points are overlapping
theme_q2r() +
scale_size_continuous(name="Shannon Diversity") +
scale_color_discrete(name="Mouse_background")
PCs %>% right_join(Fcomp2) %>%
ggplot(aes(x=PC1, y=PC2, color=Diet_Or_Water_treatment, size=shannon)) +
geom_point(alpha=0.8) + #alpha controls transparency and helps when points are overlapping
theme_q2r() +
scale_size_continuous(name="Shannon Diversity") +
scale_color_discrete(name="Iron Supply")
PCs %>% right_join(Fcomp3) %>%
ggplot(aes(x=PC1, y=PC2, color=Diet_Or_Water_treatment, size=shannon)) +
geom_point(alpha=0.8) + #alpha controls transparency and helps when points are overlapping
theme_q2r() +
scale_size_continuous(name="Shannon Diversity") +
scale_color_discrete(name="Water Supply")
PCs %>% right_join(Fcomp4) %>%
ggplot(aes(x=PC1, y=PC2, color=Infection, size=shannon)) +
geom_point(alpha=0.8) + #alpha controls transparency and helps when points are overlapping
theme_q2r() +
scale_size_continuous(name="Shannon Diversity") +
scale_color_discrete(name="Infection")
PCs %>% right_join(Fcomp5) %>%
ggplot(aes(x=PC1, y=PC2, color=Infection, size=shannon)) +
geom_point(alpha=0.8) + #alpha controls transparency and helps when points are overlapping
theme_q2r() +
scale_size_continuous(name="Shannon Diversity") +
scale_color_discrete(name="Infection")
# dev.off()- PCA for gastirc tissue:
wunifrac_pcoa <- read_qza(paste0("asv/diversity/core-metrics-results/", proj, "-weighted-unifrac-pcoa-results.qza"))
PCs <- wunifrac_pcoa$data$Vectors %>% dplyr::select(SampleID, PC1, PC2)
# pdf("b_diversity_wunifrac_PCoA_Gastric.pdf", 5, 4)
PCs %>% right_join(Gcomp1) %>%
ggplot(aes(x=PC1, y=PC2, color=Mouse_background, size=shannon)) +
geom_point(alpha=0.8) + #alpha controls transparency and helps when points are overlapping
theme_q2r() +
scale_size_continuous(name="Shannon Diversity") +
scale_color_discrete(name="Mouse_background")
PCs %>% right_join(Gcomp2) %>%
ggplot(aes(x=PC1, y=PC2, color=Diet_Or_Water_treatment, size=shannon)) +
geom_point(alpha=0.8) + #alpha controls transparency and helps when points are overlapping
theme_q2r() +
scale_size_continuous(name="Shannon Diversity") +
scale_color_discrete(name="Iron Supply")
PCs %>% right_join(Gcomp3) %>%
ggplot(aes(x=PC1, y=PC2, color=Diet_Or_Water_treatment, size=shannon)) +
geom_point(alpha=0.8) + #alpha controls transparency and helps when points are overlapping
theme_q2r() +
scale_size_continuous(name="Shannon Diversity") +
scale_color_discrete(name="Water Supply")
PCs %>% right_join(Gcomp4) %>%
ggplot(aes(x=PC1, y=PC2, color=Infection, size=shannon)) +
geom_point(alpha=0.8) + #alpha controls transparency and helps when points are overlapping
theme_q2r() +
scale_size_continuous(name="Shannon Diversity") +
scale_color_discrete(name="Infection")
PCs %>% right_join(Gcomp5) %>%
ggplot(aes(x=PC1, y=PC2, color=Infection, size=shannon)) +
geom_point(alpha=0.8) + #alpha controls transparency and helps when points are overlapping
theme_q2r() +
scale_size_continuous(name="Shannon Diversity") +
scale_color_discrete(name="Infection")
# dev.off()- Plotting a Heatmap for taxonomy
- Making a taxonomic barplot
# filter out samples with all 0 values
taxasums <- taxasums %>% select_if(~ !is.numeric(.) || sum(.) != 0)
taxa_barplot(taxasums, metadata, "Sample_type")
taxa_barplot(taxasums, metadata, "Mouse_background")
taxa_barplot(taxasums, metadata, "Infection")
taxa_barplot(taxasums, metadata, "Diet_Or_Water_treatment")
- Differential Abundance Analysis (Aldex2 method) - Volcano plot Expected Benjamini-Hochberg corrected P value of Welch’s t test was performed, p value<0.1 were shown as red dots
# set cutoff we.eBH<0.1
Pcutoff=0.1
ASVs <- read_qza(paste0("asv/dada2/",proj,"-asv-table.qza"))$data
differentials_transgene_gastric_PicoLab_Rodent_Diet <- read_qza(paste0("asv/aldex2/differentials_transgene_gastric_PicoLab_Rodent_Diet/", proj, "-differentials.qza"))$data
differentials_transgene_fecal_PicoLab_Rodent_Diet <- read_qza(paste0("asv/aldex2/differentials_transgene_fecal_PicoLab_Rodent_Diet/", proj, "-differentials.qza"))$data
differentials_iron_deficiency_gastric <- read_qza(paste0("asv/aldex2/differentials_iron_deficiency_gastric/", proj, "-differentials.qza"))$data
differentials_iron_deficiency_fecal <- read_qza(paste0("asv/aldex2/differentials_iron_deficiency_fecal/", proj, "-differentials.qza"))$data
differentials_DCA_gastric_uninfected <- read_qza(paste0("asv/aldex2/differentials_DCA_gastric_uninfected/", proj, "-differentials.qza"))$data
differentials_DCA_fecal_uninfected <- read_qza(paste0("asv/aldex2/differentials_DCA_fecal_uninfected/", proj, "-differentials.qza"))$data
differentials_infection_gastric_H20 <- read_qza(paste0("asv/aldex2/differentials_infection_gastric_H20/", proj, "-differentials.qza"))$data
differentials_infection_gastric_DCA <- read_qza(paste0("asv/aldex2/differentials_infection_gastric_DCA/", proj, "-differentials.qza"))$data
differentials_infection_fecal_H20 <- read_qza(paste0("asv/aldex2/differentials_infection_fecal_H20/", proj, "-differentials.qza"))$data
differentials_infection_fecal_DCA <- read_qza(paste0("asv/aldex2/differentials_infection_fecal_DCA/", proj, "-differentials.qza"))$data
differentials_4groups_gastric <- read_qza(paste0("asv/aldex2/differentials_4groups_gastric/", proj, "-differentials.qza"))$data
differentials_4groups_fecal <- read_qza(paste0("asv/aldex2/differentials_4groups_fecal/", proj, "-differentials.qza"))$data
taxonomy <- read_qza(paste0("asv/taxonomy/", proj, "-taxonomy.qza"))$data
tree <- read_qza(paste0("asv/phylogeny/", proj, "-rooted_tree.qza"))$data- How does the gastrin transgene effect the gastric and intestinal microbiota?
- Gastric samples: compare 14.1 G-14.9 G (uninfected WT FVBN mice) vs. 14.10 G-14.14 G (uninfected INS-GAS mice with gastrin transgene).
## number of significantly changed taxa: 1## log2FC q-value
## 1 9.9766 0.05559636
## Taxon
## 1 k__Bacteria; p__Firmicutes; c__Bacilli; o__Turicibacterales; f__Turicibacteraceae; g__Turicibacter; s__
- Fecal samples: compare 14.1 F-14.9 F (uninfected WT FVBN mice) vs. 14.10 F-14.14 F (uninfected INS-GAS mice with gastrin transgene).
## number of significantly changed taxa: 16## log2FC q-value
## 1 13.699216 0.0005407524
## 2 13.905920 0.0010027803
## 3 9.325503 0.0408352107
## 4 1.230751 0.0261901206
## 5 8.406816 0.0051649611
## 6 4.523676 0.0925379593
## 7 1.655063 0.0751760076
## 8 8.751153 0.0049225773
## 9 11.194655 0.0156877468
## 10 -11.774863 0.0400461573
## 11 9.110576 0.0040563797
## 12 -10.119853 0.0806550201
## 13 -10.113836 0.0693734726
## 14 4.313235 0.0939859903
## 15 -9.003194 0.0650795308
## 16 -8.195831 0.0833762656
## Taxon
## 1 k__Bacteria; p__Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Rikenellaceae; g__; s__
## 2 k__Bacteria; p__Tenericutes; c__Mollicutes; o__Anaeroplasmatales; f__Anaeroplasmataceae; g__Anaeroplasma; s__
## 3 k__Bacteria; p__Firmicutes; c__Bacilli; o__Turicibacterales; f__Turicibacteraceae; g__Turicibacter; s__
## 4 k__Bacteria; p__Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__S24-7; g__; s__
## 5 k__Bacteria; p__Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__S24-7; g__; s__
## 6 k__Bacteria; p__Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__S24-7; g__; s__
## 7 k__Bacteria; p__Proteobacteria; c__Deltaproteobacteria; o__Desulfovibrionales; f__Desulfovibrionaceae; g__Bilophila; s__
## 8 k__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae
## 9 k__Bacteria; p__Proteobacteria; c__Alphaproteobacteria; o__RF32; f__; g__; s__
## 10 k__Bacteria; p__Tenericutes; c__Mollicutes; o__Anaeroplasmatales; f__Anaeroplasmataceae; g__Anaeroplasma; s__
## 11 k__Bacteria; p__Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__S24-7; g__; s__
## 12 k__Bacteria; p__Deferribacteres; c__Deferribacteres; o__Deferribacterales; f__Deferribacteraceae; g__Mucispirillum; s__schaedleri
## 13 k__Bacteria
## 14 k__Bacteria; p__Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__S24-7; g__; s__
## 15 k__Bacteria; p__Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__; g__; s__
## 16 k__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales
- How does iron deficiency effect the gastric and intestinal microbiota?
- Gastric samples: compare 14.15 G-14.19 G (uninfected INS-GAS mice on iron-replete diet) vs. 14.20 G-14.26 G (uninfected INS-GAS mice on iron-depleted diet).
## number of significantly changed taxa: 3## log2FC q-value
## 1 -9.375134 0.03015816
## 2 -11.481620 0.02818266
## 3 -11.099921 0.03223550
## Taxon
## 1 k__Bacteria; p__Verrucomicrobia; c__Verrucomicrobiae; o__Verrucomicrobiales; f__Verrucomicrobiaceae; g__Akkermansia; s__muciniphila
## 2 k__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Peptostreptococcaceae; g__; s__
## 3 k__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Peptostreptococcaceae; g__; s__
- Fecal samples: compare 14.15 F-14.19 F (uninfected INS-GAS mice on iron-replete diet) vs. 14.20 F-14.26 F (uninfected INS-GAS mice on iron-depleted diet).
## number of significantly changed taxa: 11## log2FC q-value
## 1 -16.207116 0.001389867
## 2 16.047979 0.012022007
## 3 13.091172 0.022666083
## 4 -7.794630 0.086415169
## 5 -1.090135 0.038824655
## 6 -8.653676 0.016371031
## 7 -11.379194 0.006250233
## 8 -1.793053 0.036625401
## 9 -10.616931 0.009127430
## 10 9.436136 0.050834965
## 11 -6.951504 0.093878169
## Taxon
## 1 k__Bacteria; p__Verrucomicrobia; c__Verrucomicrobiae; o__Verrucomicrobiales; f__Verrucomicrobiaceae; g__Akkermansia; s__muciniphila
## 2 k__Bacteria; p__Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__; g__; s__
## 3 k__Bacteria; p__Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Bacteroidaceae; g__Bacteroides; s__acidifaciens
## 4 k__Bacteria; p__Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__S24-7; g__; s__
## 5 k__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae
## 6 k__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae
## 7 k__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Peptostreptococcaceae; g__; s__
## 8 k__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales
## 9 k__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Peptostreptococcaceae; g__; s__
## 10 k__Bacteria; p__Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__S24-7; g__; s__
## 11 k__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae
- What effect does DCA have on the gastric and intestinal microbiota?
- Gastric samples: compare 16.1 G – 16.5 G (uninfected INS-GAS mice given water alone) vs. 16.6 G – 16.10 G (uninfected INS-GAS mice give water supplemented with 100 uM DCA).
## number of significantly changed taxa: 0## [1] log2FC q-value Taxon
## <0 rows> (or 0-length row.names)
b.Fecal samples: compare 16.1 F – 16.5 F (uninfected INS-GAS mice given water alone) vs. 16.6 F – 16.10 F (uninfected INS-GAS mice given water supplemented with 100 uM DCA).
## number of significantly changed taxa: 0## [1] log2FC q-value Taxon
## <0 rows> (or 0-length row.names)
- What effect does H. pylori have on the gastric and intestinal microbiota? (NOTE: some of these animals we were unable to culture back H. pylori. You may be able to see if it’s worth including in the comparisons if you don’t see Helicobacter 16S sequences in your analysis).
- Gastric samples: compare 16.1 G – 16.5 G (uninfected INS-GAS mice given water alone) vs. 16.11 G – 16.15 G (H. pylori infected INS-GAS mice given water alone)
## number of significantly changed taxa: 0## [1] log2FC q-value Taxon
## <0 rows> (or 0-length row.names)
- Gastric samples: compare 16.6 G- 16.10 G (uninfected INS-GAS mice give water supplemented with 100 uM DCA) vs. 16.16 G – 16.20 G (H. pylori infected INS-GAS mice give water supplemented with 100 uM DCA).
## number of significantly changed taxa: 0## [1] log2FC q-value Taxon
## <0 rows> (or 0-length row.names)
- Fecal samples: compare 16.1 F – 16.5 F (uninfected INS-GAS mice given water alone) vs. 16.11 F – 16.15 F (H. pylori infected INS-GAS mice given water alone)
## number of significantly changed taxa: 0## [1] log2FC q-value Taxon
## <0 rows> (or 0-length row.names)
d. Fecal samples: compare 16.6 F- 16.10 F (uninfected INS-GAS mice give water supplemented with 100 uM DCA) vs. 16.16 F – 16.20 F (H. pylori infected INS-GAS mice give water supplemented with 100 uM DCA).## number of significantly changed taxa: 0## [1] log2FC q-value Taxon
## <0 rows> (or 0-length row.names)
- Overall differences in the gastric among the 4 groups: a. 16.1 G -16.5 G (uninfected INS-GAS mice given water alone) b. 16.6 G- 16.10 G (uninfected INS-GAS mice give water supplemented with 100 uM DCA) c. 16.11 G – 16.15 G (H. pylori infected INS-GAS mice given water alone)
- 16.16 G – 16.20 G (H. pylori infected INS-GAS mice give water supplemented with 100 uM DCA)
## number of significantly changed taxa: 0## [1] log2FC q-value Taxon
## <0 rows> (or 0-length row.names)
- Overall differences in the intestinal microbiota in the 4 groups: a. 16.1 F -16.5 F (uninfected INS-GAS mice given water alone) b. 16.6 F- 16.10 F (uninfected INS-GAS mice give water supplemented with 100 uM DCA) c. 16.11 F – 16.15 F (H. pylori infected INS-GAS mice given water alone)
- 16.16 F – 16.20 F (H. pylori infected INS-GAS mice give water supplemented with 100 uM DCA)
## number of significantly changed taxa: 0## [1] log2FC q-value Taxon
## <0 rows> (or 0-length row.names)